GPU-Supported Object Tracking Using Adaptive Appearance Models and Particle Swarm Optimization

  • Boguslaw Rymut
  • Bogdan Kwolek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)


This paper demonstrates how CUDA-capable Graphics Processor Unit can be effectively used to accelerate a tracking algorithm based on adaptive appearance models. The object tracking is achieved by particle swarm optimization algorithm. Experimental results show that the GPU implementation of the algorithm exhibits a more than 40-fold speed-up over the CPU implementation.


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  1. 1.
    Weng, S., Kuo, C., Tu, S.: Video object tracking using adaptive Kalman filter. J. Vis. Comun. Image Represent. 17, 1190–1208 (2006)CrossRefGoogle Scholar
  2. 2.
    Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. Int. J. of Computer Vision 29, 5–28 (2006)CrossRefGoogle Scholar
  3. 3.
    Jepson, A.D., Fleet, D.J., El-Maraghi, T.: Robust on-line appearance models for visual tracking. IEEE Trans. on PAMI 25, 1296–1311 (2003)Google Scholar
  4. 4.
    Zhang, X., Hu, W., Maybank, S., Li, X., Zhu, M.: Sequential particle swarm optimization for visual tracking. In: IEEE Int. Conf. on CVPR, pp. 1–8 (2008)Google Scholar
  5. 5.
    Kwolek, B.: Particle swarm optimization-based object tracking. Fundamenta Informaticae 95, 449–463 (2009)MathSciNetGoogle Scholar
  6. 6.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Statistical Society. Series B 39, 1–38 (1977)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, pp. 1942–1948. IEEE Press, Piscataway (1995)CrossRefGoogle Scholar
  8. 8.
    Wasson, S.: Nvidia’s GeForce 8800 graphics processor. Technical report, PC Hardware Explored (2006)Google Scholar
  9. 9.
    Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with CUDA. ACM Queue 6, 40–53 (2008)CrossRefGoogle Scholar
  10. 10.
    Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Transactions on Modeling and Computer Simulation 8, 3–30 (1998)zbMATHCrossRefGoogle Scholar
  11. 11.
    Box, G.E.P., Muller, M.E.: A note on the generation of random normal deviates. The Annals of Mathematical Statistics 29, 610–611 (1958)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Boguslaw Rymut
    • 1
  • Bogdan Kwolek
    • 1
  1. 1.Rzeszów University of TechnologyRzeszówPoland

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